1. Gangaraj, S. and Farrahi, G., "Side effects of shot peening on fatigue crack initiation life", International Journal of Engineering-Transactions A: Basics, Vol. 24, No. 3, (2011), 275-284.
2. Maleki, E. and Zabihollah, A., "Modeling of shot-peening effects on the surface properties of a (TiB + TiC)/Ti–6Al–4V composite employing artificial neural networks", Materiali in Tehnologije, Vol. 50, No. 6, (2016), 851-860.
3. Nam, Y.-S., Jeon, U., Yoon, H.-K., Shin, B.-C. and Byun, J.-H., "Use of response surface methodology for shot peening process optimization of an aircraft structural part", The International Journal of Advanced Manufacturing Technology, Vol. 87, No. 9-12, (2016), 2967-2981.
4. Farrahi, G., Lebrijn, J. and Couratin, D., "Effect of shot peening on residual stress and fatigue life of a spring steel", Fatigue & Fracture of Engineering Materials & Structures, Vol. 18, No. 2, (1995), 211-220.
5. Bagherifard, S. and Guagliano, M., "Fatigue behavior of a low-alloy steel with nanostructured surface obtained by severe shot peening", Engineering Fracture Mechanics, Vol. 81, (2012), 56-68.
6. Bagherifard, S., Fernandez-Pariente, I., Ghelichi, R. and Guagliano, M., "Effect of severe shot peening on microstructure and fatigue strength of cast iron", International Journal of Fatigue, Vol. 65, (2014), 64-70.
7. Bagherifard, S., Slawik, S., Fernández-Pariente, I., Pauly, C., Mücklich, F. and Guagliano, M., "Nanoscale surface modification of aisi 316l stainless steel by severe shot peening", Materials & Design, Vol. 102, (2016), 68-77.
8. Unal, O. and Varol, R., "Almen intensity effect on microstructure and mechanical properties of low carbon steel subjected to severe shot peening", Applied Surface Science, Vol. 290, (2014), 40-47.
9. Unal, O. and Varol, R., "Surface severe plastic deformation of aisi 304 via conventional shot peening, severe shot peening and repeening", Applied Surface Science, Vol. 351, (2015), 289-295.
10. Hassani-Gangaraj, S., Moridi, A., Guagliano, M., Ghidini, A. and Boniardi, M., "The effect of nitriding, severe shot peening and their combination on the fatigue behavior and micro-structure of a low-alloy steel", International Journal of Fatigue, Vol. 62, (2014), 67-76.
11. Skinner, A. and Broughton, J., "Neural networks in computational materials science: Training algorithms", Modelling and Simulation in Materials Science and Engineering, Vol. 3, No. 3, (1995), 371-380.
12. Xiao, X., Liu, G., Hu, B., Zheng, X., Wang, L., Chen, S. and Ullah, A., "A comparative study on arrhenius-type constitutive equations and artificial neural network model to predict high-temperature deformation behaviour in 12Cr3WV steel", Computational Materials Science, Vol. 62, (2012), 227-234.
13. Restrepo, S.E., Giraldo, S.T. and Thijsse, B.J., "Using artificial neural networks to predict grain boundary energies", Computational Materials Science, Vol. 86, (2014), 170-173.
14. Sidhu, G., Bhole, S., Chen, D. and Essadiqi, E., "Determination of volume fraction of bainite in low carbon steels using artificial neural networks", Computational Materials Science, Vol. 50, No. 12, (2011), 3377-3384.
15. Han, Y., Qiao, G., Sun, J. and Zou, D., "A comparative study on constitutive relationship of as-cast 904l austenitic stainless steel during hot deformation based on arrhenius-type and artificial neural network models", Computational Materials Science, Vol. 67, (2013), 93-103.
16. Akbarpour, H., Mohajeri, M. and Moradi, M., "Investigation on the synthesis conditions at the interpore distance of nanoporous anodic aluminum oxide: A comparison of experimental study, artificial neural network, and multiple linear regression", Computational Materials Science, Vol. 79, (2013), 75-81.
17. Velez, J.F. and Powell, G.W., "Some metallographic observations on the spalling of aisi 1060 steel by the formation of adiabatic shear bands", Wear, Vol. 66, No. 3, (1981), 367-378.
18. Roy, H., Parida, N., Sivaprasad, S., Tarafder, S. and Ray, K., "Acoustic emissions during fracture toughness tests of steels exhibiting varying ductility", Materials Science and Engineering: A, Vol. 486, No. 1, (2008), 562-571.
19. J443, S., Procedures for using standard shot peening almen test strip, SAE International.
20. Sun, Y., Zeng, W., Han, Y., Ma, X., Zhao, Y., Guo, P., Wang, G. and Dargusch, M.S., "Determination of the influence of processing parameters on the mechanical properties of the ti–6al–4v alloy using an artificial neural network", Computational Materials Science, Vol. 60, (2012), 239-244.
21. Maleki, E. and Maleki, N., "Artificial neural network modeling of Pt/C cathode degradation in pem fuel cells", Journal of Electronic Materials, Vol. 45, No. 8, (2016), 3822-3834.
22. Jahanshahi, M., Maleki, E. and Ghiami, A., "On the efficiency of artificial neural networks for plastic analysis of planar frames in comparison with genetic algorithms and ant colony systems", Neural Computing and Applications, Vol. 28, No. 11, (2017), 3209-3227.
23. Zhao, J., Ding, H., Zhao, W., Huang, M., Wei, D. and Jiang, Z., "Modelling of the hot deformation behaviour of a titanium alloy using constitutive equations and artificial neural network", Computational Materials Science, Vol. 92, (2014), 47-56.
24. Esmailzadeh, M. and Aghaie-Khafri, M., "Finite element and artificial neural network analysis of ECAP", Computational Materials Science, Vol. 63, (2012), 127-133.
25. Benyelloul, K. and Aourag, H., "Elastic constants of austenitic stainless steel: Investigation by the first-principles calculations and the artificial neural network approach", Computational Materials Science, Vol. 67, (2013), 353-358.
26. Maleki, E. and Sherafatnia, K., "Investigation of single and dual step shot peening effects on mechanical and metallurgical properties of 18crnimo7-6 steel using artificial neural network", Int. J. Mater. Mech. Manuf, Vol. 4, (2016), 100-105.
27. Abendroth, M. and Kuna, M., "Determination of deformation and failure properties of ductile materials by means of the small punch test and neural networks", Computational Materials Science, Vol. 28, No. 3, (2003), 633-644.
28. Maleki, N., Kashanian, S., Maleki, E. and Nazari, M., "A novel enzyme based biosensor for catechol detection in water samples using artificial neural network", Biochemical Engineering Journal, Vol. 128, (2017), 1-11.
29. Elangovan, K., Narayanan, C.S. and Narayanasamy, R., "Modelling of forming limit diagram of perforated commercial pure aluminium sheets using artificial neural network", Computational Materials Science, Vol. 47, No. 4, (2010), 1072-1078.
30. Maleki, E., "Artificial neural networks application for modeling of friction stir welding effects on mechanical properties of 7075-t6 aluminum alloy", in IOP Conference Series: Materials Science and Engineering, IOP Publishing. Vol. 103, (2015), 012034.
31. Maleki, E., Farrahi, G.H. and Sherafatnia, K., Application of artificial neural network to predict the effects of severe shot peening on properties of low carbon steel, in Machining, joining and modifications of advanced materials. (2016), 45-60.
32. Saitoh, H., Ochi, T. and Kubota, M., "Formation of surface nanocrystalline structure in steels by air blast shot peening", in Proceedings of the 10th international conference on shot peening, Japan., (2008), 488-493.
33. Wang, Y. and Ma, E., "Three strategies to achieve uniform tensile deformation in a nanostructured metal", Acta Materialia, Vol. 52, No. 6, (2004), 1699-1709.